Data 607 Project 3
Sung Lee
3/21/2020
Assignment on RPubs
Rmd on GitHub
Introduction
These are the libraries we will be using.
library(RMySQL)
library(tm)
library(SnowballC)
library(wordcloud)
library(RColorBrewer)
library(stringr)
library(RWeka)
library(ggplot2)
library(tidytext)
library(tidyverse)
library(knitr)
library(quanteda)Approach
We had discussions about which sets of data to use. The internet is filled with a plethora of data, but few were of applicable use. Linkedin was not readily accessible.
Importing
The data we ended up using is from the web site https://data.world/jobspikr/10000-data-scientist-job-postings-from-the-usa. Phil was able to obtain the csv. The data was scraped using JobsPikr. The csv consists of 10,000 records and had the following as text fields: crawl_timestamp, url, job_title, category, company_name, city, state, country, inferred_city, inferred_state, inferred_country, post_date, job_description, job_type, salary_offered, job_board, geo, cursor, contact_email, contact_phone_number, uniq_id, and html_job_desc.
This is the original csv with field names.
CSV Fieldnames
Inital import of the csv file.
# Using read_csv as it allows for on the fly decompression of zip csv files
jobs <- read_csv("https://github.com/logicalschema/DATA607/raw/master/project3/data/data_scientist_united_states_job_postings_jobspikr.csv.gz")## Parsed with column specification:
## cols(
## .default = col_character(),
## post_date = col_date(format = ""),
## salary_offered = col_logical(),
## cursor = col_double(),
## contact_email = col_logical(),
## html_job_description = col_logical()
## )## See spec(...) for full column specifications.# Look that the first group of imported rows
head(jobs)Initial problems and solutions:
The csv file is 48.5 MB and the character set had UTF-8 characters.
The file was zipped up to reduce download time. When the file is imported into R, we will tidy the data.
HTML code was left in the
job_descriptionfield. Evidence of the HTML scrape of the data source.Invalid characters will be removed.
Initial load of the csv into R would take time depending upon the hardware of the group member’s system.
With the zip file and cutting up the file into smaller pieces, this would reduce the need for additional RAM.
How would we convert to a normalized database?
The csv was converted to a MySQL script file. The header column names were encapsulated with
"marks. Workbench was used to create a new table on the MySQL database. Subsequently, through SQL SELECT, we normalized the data.In addition, when the data was imported in a database, Sung ran Text Analytics on the
job_descriptioncolumn to find key phrases. This information was used to create a new column calledkeyphrasesin the normalized database.Some group member’s machines did not have the same amount of RAM. Vanita’s laptop had 8 GB RAM and Sung’s laptop was running 16 GB.
How did the data look in Azure?
When the data was imported into Microsoft’s cloud service, we ran some initial tests on the data to look for patterns. Using Microsoft’s Text Analytics1 tools the following is a word cloud2 of the keyphrases that were discovered by Microsoft’s AI:
Word Cloud Courtesy of Azure
The key phrases were stored in the database. Key phrases were extrapolated from the job descriptions for each of the jobs listed. Because the use of Microsoft’s service was limited, we found the need for persistent storage of the results. Sung had used up his $200 credit for one of his subscriptions within a week of testing.
Excursus
This is a brief description of some of the steps taken to help alleviate the problems of importing the information for use in our project.
Infrastructure
Azure, MySQL, and Cognitive Text Analytics Sung wanted to experiment with cloud computing and so he used his SPS email to create a trial account for Azure. He created a MySQL database instance to host the databases used. Each trial account is given a $200 credit.
MySQL database access This connection is made public but is restricted by IP address. If you desire access, please email one of the Team members.
Server: data607.mysql.database.azure.com
Port: 3306
Username: data607@data607
Password: student#2020
Default timeout settings for the database server had to be lengthened to enable longer processing.
This is a link to the mysqldump of the database.
Diagram of Database 
Tools
Microsoft’s Cognitive Text Analytics, Power BI, Slack, GitHub, and MySQL Workbench In addition to the database instance, he created, Sung created an instance for Azure’s Cognitive Text Analytics to experiment to see what information Microsoft’s AI can mine from our data. The tools were used to facilitate handling the data.
Power BI is a Microsoft product that is used to visualize data. It was employed to work with Microsoft’s Cognitive Text Analytics to extrapolate keyphrases from the job_descriptions of the data and to create a simple word cloud to compare with our later analysis.
Slack and GitHub were used to collaborate. Files were exchanged via Slack. Code was edited via GitHub.
MySQL Workbench was used to connect to the MySQL database.
Data Management
As an option to curtail the amount of time needed to process the 10,000 row csv, group members worked with a small subset of the file, tested code, and then would work with the larger data set. Some group members machines would complain about memory errors.
Files being worked on from Github were compressed to enable quicker transport across the network.
This is an example of a memory error on Sung’s laptop when trying to text mine on the job description data: 
Tidying
Since we have imported our data into a database, we still need to clean up the job descriptions. We hope that we will be able to tidy the data such that we would be able to determine frequencies and see what picture the data paints for us.
We will now begin to query the database for the job descriptions and the key phrases that Azure extrapolated for us. We will store job descriptions in the variable descriptions and the key phrases in keyphrases.
#Form a connection to the MySQL database
mydb <- dbConnect(MySQL(),
user='data607@data607',
password='student#2020',
dbname='project3data607',
host='data607.mysql.database.azure.com'
)
#Key phrases is stored in the meta table of the database
rs <- dbSendQuery(mydb, "SELECT keyphrases FROM meta")
#Remove argument for n = -1 if you don't want all the records in the db
#dbRows <- dbFetch(rs, n = -1 )
dbRows <- dbFetch(rs)
dbClearResult(rs)## [1] TRUE#Job descriptions are stored in the job table of the database
rs <- dbSendQuery(mydb, "SELECT job_description FROM job")
#Remove argument for n = -1 if you don't want all the records in the db
#descriptions <- dbFetch(rs, n = -1)
descriptions <- dbFetch(rs)
dbClearResult(rs)## [1] TRUE#This a sample of the Key Phrases from Azure
kable(dbRows[1:5, ],
caption = "Job Description Key Phrases",
col.names = c("Key Phrases")
)| Key Phrases |
|---|
| logistics-related data analysis, data integrity, historical data, data scientist, hard data, internal reporting, data science role, external reporting, analysis processes, ongoing reporting, transportation analysis projects, in-depth logistics analytics, predictive logistics pricing models, transportation bids, analytical tools, years of robust analytical experience, advanced degree, operations team, ability, theoretical client distribution models, carrier bids, in-depth investigations, s degree, computer science, potential opportunities, quantitative skills, similar quantitative field, client cost reduction, institutional analytics capabilities, methodologies relevant, process improvements, optimization, mathematics, engineering, statistics, customer, corporate finance, pandas, current tms software provider, growth, business problems, assumptions, things, design, vision, python, focus, validation, metrics, eye, financial impacts, best practices, recommendations, drive actions, benefits, vacation, proficiency, bachelorâ, example, automation, company, supply chain, location, norwell, mysql, candidate, documentation, position, time, desired background, aborn, compensation, salary |
| data science, data analyses, data scientists, significant experience, talented data scientist, big data tools, data mining problems, significant programming experience, machine learning software libraries, large amounts of complex data, advanced data analytics division, challenging machine learning, related libraries, related algorithms, deep learning algorithms, s degree, reston, extensible software, innovative designs, employment individuals, results of innovative solutions, software developers, related field, qualified applicants, s entire process, computer science, algorithm design, protected veterans, off-the-shelf solutions, people, d projects, sexual orientation, gender identity, classification, national origin, religion, scalable anomaly detection, concept, recommendation, minorities, performance metrics, women, java, clientsâ, mathematics, knowledge of best practices, disability, color, policy, freedom, apache spark, statistics, python, mathematicians, masterâ, tensorflow, scikit, eligibility requirements, affirmative action, support, classified information, access, projectâ, nlp, natural language processing, torch, oral reports, basis of race, ideal candidate, metron, mahout, implementation, demonstration, products, apis, disabilities, position location, consideration, security investigation, veteran status, vevraa federal contractor, project teams, management structure, deeplearning4j, us citizenship, bachelorâ, job description, desired qualifications, person, weka |
| data science, data analyses, data scientists, significant experience, talented data scientist, big data tools, data mining problems, significant programming experience, machine learning software libraries, large amounts of complex data, advanced data analytics division, challenging machine learning, related libraries, related algorithms, deep learning algorithms, s degree, reston, extensible software, innovative designs, employment individuals, results of innovative solutions, software developers, related field, qualified applicants, s entire process, computer science, algorithm design, protected veterans, off-the-shelf solutions, people, d projects, sexual orientation, gender identity, classification, national origin, religion, scalable anomaly detection, concept, recommendation, minorities, performance metrics, women, java, clientsâ, mathematics, knowledge of best practices, disability, color, policy, freedom, apache spark, statistics, python, mathematicians, masterâ, tensorflow, scikit, eligibility requirements, affirmative action, support, classified information, access, projectâ, nlp, natural language processing, torch, oral reports, basis of race, ideal candidate, metron, mahout, implementation, demonstration, products, apis, disabilities, position location, consideration, security investigation, veteran status, vevraa federal contractor, project teams, management structure, deeplearning4j, us citizenship, bachelorâ, job description, desired qualifications, person, weka |
| data visualizations, interpreting data, data wrangling, data capture firm, rigorous data capture, unstructured data sets, ssrs data scientist, reports of data metrics, experienced data scientist, data visualization tools, leading data-driven survey research, growing team of skilled data capture professionals, research outcomes, machine learning methods, cluster analysis, analysis of variance, s degree, bagging methods, simulation methods, appropriate methods, modeling process, years of related work experience, time series analysis, business case analysis, year degree, quantitative modeling, successful modeling, non-partisan research areas, challenging research needs, trends, python, hands, advanced mathematical techniques, public policy, public opinion, logistics regression, problem identification, statistical analyses, random forest, neural networks, correlation, decision trees, health care, feature engineering, clientsâ, bootstrap, matplotlib, nosql dbs, prescriptive analytic solutions, technical bachelorâ, oracle, algorithms, teradata, monte carlo, information technology field of study, graphical representation, insight generation, ggplot, models, databases, exposure, sas, tableau, masterâ, outliers, srss, ability, dashboards, key responsibilities, qualifications |
| data visualizations, interpreting data, data wrangling, data capture firm, rigorous data capture, unstructured data sets, ssrs data scientist, reports of data metrics, experienced data scientist, data visualization tools, leading data-driven survey research, growing team of skilled data capture professionals, research outcomes, machine learning methods, cluster analysis, analysis of variance, s degree, bagging methods, simulation methods, appropriate methods, modeling process, years of related work experience, time series analysis, business case analysis, year degree, quantitative modeling, successful modeling, non-partisan research areas, challenging research needs, trends, python, hands, advanced mathematical techniques, public policy, public opinion, logistics regression, problem identification, statistical analyses, random forest, neural networks, correlation, decision trees, health care, feature engineering, clientsâ, bootstrap, matplotlib, nosql dbs, prescriptive analytic solutions, technical bachelorâ, oracle, algorithms, teradata, monte carlo, information technology field of study, graphical representation, insight generation, ggplot, models, databases, exposure, sas, tableau, masterâ, outliers, srss, ability, dashboards, key responsibilities, qualifications |
Azure Key Phrases
Let’s get the unique key phrases that Azure found.
#Grab the unique keyphrases after some clean up:
keyphrases <- unlist(str_split(dbRows, ","))## Warning in stri_split_regex(string, pattern, n = n, simplify = simplify, :
## argument is not an atomic vector; coercingkeyphrases <- str_replace_all(keyphrases, "^ ", "")
keyphrases <- str_replace_all(keyphrases, "[^\x20-\x7E]", "")
keyphrases <- str_replace_all(keyphrases, "\"", "")
# Remove the "c(" in the first element
keyphrases[[1]] <- keyphrases[[1]] %>%
str_replace("c[(]", "")
keyphrases <- as.data.frame(keyphrases)
#Some of the key phrases
keyphraseslibrary(summarytools)## Registered S3 method overwritten by 'pryr':
## method from
## print.bytes Rcpp## For best results, restart R session and update pander using devtools:: or remotes::install_github('rapporter/pander')##
## Attaching package: 'summarytools'## The following object is masked from 'package:tibble':
##
## viewsummarytools::freq(keyphrases$keyphrases, order = "freq")## Frequencies
## keyphrases$keyphrases
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ------------------------------------------------------------------------------------ ------- --------- -------------- --------- --------------
## python 355 0.68 0.68 0.68 0.68
## computer science 311 0.59 1.27 0.59 1.27
## statistics 214 0.41 1.68 0.41 1.68
## data scientist 199 0.38 2.05 0.38 2.05
## mathematics 167 0.32 2.37 0.32 2.37
## s degree 162 0.31 2.68 0.31 2.68
## responsibilities 143 0.27 2.95 0.27 2.95
## sql 139 0.26 3.22 0.26 3.22
## religion 130 0.25 3.47 0.25 3.47
## ability 125 0.24 3.70 0.24 3.70
## color 125 0.24 3.94 0.24 3.94
## sexual orientation 124 0.24 4.18 0.24 4.18
## national origin 120 0.23 4.41 0.23 4.41
## master 116 0.22 4.63 0.22 4.63
## algorithms 115 0.22 4.85 0.22 4.85
## tableau 114 0.22 5.06 0.22 5.06
## gender identity 113 0.22 5.28 0.22 5.28
## years of experience 112 0.21 5.49 0.21 5.49
## customers 109 0.21 5.70 0.21 5.70
## race 109 0.21 5.91 0.21 5.91
## bachelor 106 0.20 6.11 0.20 6.11
## sas 106 0.20 6.31 0.20 6.31
## disability 104 0.20 6.51 0.20 6.51
## veteran status 104 0.20 6.71 0.20 6.71
## qualifications 101 0.19 6.90 0.19 6.90
## people 100 0.19 7.09 0.19 7.09
## equal opportunity employer 97 0.18 7.27 0.18 7.27
## spark 96 0.18 7.46 0.18 7.46
## java 95 0.18 7.64 0.18 7.64
## engineering 90 0.17 7.81 0.17 7.81
## machine learning 89 0.17 7.98 0.17 7.98
## opportunities 86 0.16 8.14 0.16 8.14
## related field 85 0.16 8.30 0.16 8.30
## hadoop 84 0.16 8.46 0.16 8.46
## company 83 0.16 8.62 0.16 8.62
## design 81 0.15 8.78 0.15 8.78
## passion 81 0.15 8.93 0.15 8.93
## economics 80 0.15 9.08 0.15 9.08
## position 80 0.15 9.23 0.15 9.23
## phd 79 0.15 9.38 0.15 9.38
## world 79 0.15 9.53 0.15 9.53
## data mining 78 0.15 9.68 0.15 9.68
## results 78 0.15 9.83 0.15 9.83
## clients 76 0.14 9.98 0.14 9.98
## clustering 76 0.14 10.12 0.14 10.12
## employees 76 0.14 10.27 0.14 10.27
## proficiency 76 0.14 10.41 0.14 10.41
## large data sets 75 0.14 10.55 0.14 10.55
## physics 73 0.14 10.69 0.14 10.69
## we 71 0.14 10.83 0.14 10.83
## years 71 0.14 10.96 0.14 10.96
## requirements 70 0.13 11.10 0.13 11.10
## role 69 0.13 11.23 0.13 11.23
## tensorflow 69 0.13 11.36 0.13 11.36
## predictive modeling 67 0.13 11.49 0.13 11.49
## regard 67 0.13 11.61 0.13 11.61
## regression 67 0.13 11.74 0.13 11.74
## data scientists 66 0.13 11.87 0.13 11.87
## decision trees 66 0.13 11.99 0.13 11.99
## you 66 0.13 12.12 0.13 12.12
## models 65 0.12 12.24 0.12 12.24
## hive 64 0.12 12.36 0.12 12.36
## neural networks 62 0.12 12.48 0.12 12.48
## data analysis 60 0.11 12.60 0.11 12.60
## education 59 0.11 12.71 0.11 12.71
## statistical analysis 59 0.11 12.82 0.11 12.82
## access 58 0.11 12.93 0.11 12.93
## ideal candidate 58 0.11 13.04 0.11 13.04
## job description 58 0.11 13.15 0.11 13.15
## trends 58 0.11 13.26 0.11 13.26
## random forest 57 0.11 13.37 0.11 13.37
## bachelor's degree 56 0.11 13.48 0.11 13.48
## familiarity 56 0.11 13.58 0.11 13.58
## leadership 55 0.10 13.69 0.10 13.69
## tools 54 0.10 13.79 0.10 13.79
## candidates 53 0.10 13.89 0.10 13.89
## data visualization tools 53 0.10 13.99 0.10 13.99
## experience 53 0.10 14.09 0.10 14.09
## predictive models 53 0.10 14.20 0.10 14.20
## big data 52 0.10 14.29 0.10 14.29
## experienced data scientist 52 0.10 14.39 0.10 14.39
## expertise 52 0.10 14.49 0.10 14.49
## recommendations 52 0.10 14.59 0.10 14.59
## benefits 51 0.10 14.69 0.10 14.69
## databases 51 0.10 14.79 0.10 14.79
## hands 51 0.10 14.88 0.10 14.88
## natural language processing 51 0.10 14.98 0.10 14.98
## opportunity 51 0.10 15.08 0.10 15.08
## c++ 50 0.10 15.17 0.10 15.17
## dashboards 50 0.10 15.27 0.10 15.27
## key responsibilities 50 0.10 15.36 0.10 15.36
## actionable insights 49 0.09 15.46 0.09 15.46
## deep learning 49 0.09 15.55 0.09 15.55
## matlab 49 0.09 15.64 0.09 15.64
## unstructured data 49 0.09 15.74 0.09 15.74
## aws 48 0.09 15.83 0.09 15.83
## scala 48 0.09 15.92 0.09 15.92
## willingness 48 0.09 16.01 0.09 16.01
## consideration 47 0.09 16.10 0.09 16.10
## insights 47 0.09 16.19 0.09 16.19
## oracle 47 0.09 16.28 0.09 16.28
## classification 46 0.09 16.37 0.09 16.37
## data science 46 0.09 16.45 0.09 16.45
## exposure 46 0.09 16.54 0.09 16.54
## organization 46 0.09 16.63 0.09 16.63
## projects 46 0.09 16.72 0.09 16.72
## qualified applicants 46 0.09 16.80 0.09 16.80
## statistical analyses 46 0.09 16.89 0.09 16.89
## techniques 46 0.09 16.98 0.09 16.98
## employment 45 0.09 17.06 0.09 17.06
## complex problems 44 0.08 17.15 0.08 17.15
## applicants 43 0.08 17.23 0.08 17.23
## industry 43 0.08 17.31 0.08 17.31
## matplotlib 43 0.08 17.39 0.08 17.39
## products 43 0.08 17.48 0.08 17.48
## quantitative field 43 0.08 17.56 0.08 17.56
## help 42 0.08 17.64 0.08 17.64
## integrity 42 0.08 17.72 0.08 17.72
## marital status 42 0.08 17.80 0.08 17.80
## math 42 0.08 17.88 0.08 17.88
## pandas 42 0.08 17.96 0.08 17.96
## peers 42 0.08 18.04 0.08 18.04
## plus 42 0.08 18.12 0.08 18.12
## ggplot 41 0.08 18.20 0.08 18.20
## machine learning methods 41 0.08 18.27 0.08 18.27
## patterns 41 0.08 18.35 0.08 18.35
## preferred qualifications 41 0.08 18.43 0.08 18.43
## services 41 0.08 18.51 0.08 18.51
## time 41 0.08 18.59 0.08 18.59
## applied mathematics 40 0.08 18.66 0.08 18.66
## data analytics 40 0.08 18.74 0.08 18.74
## data wrangling 40 0.08 18.81 0.08 18.81
## order 40 0.08 18.89 0.08 18.89
## processes 40 0.08 18.97 0.08 18.97
## solutions 40 0.08 19.04 0.08 19.04
## teradata 40 0.08 19.12 0.08 19.12
## areas 39 0.07 19.19 0.07 19.19
## data visualizations 39 0.07 19.27 0.07 19.27
## following 39 0.07 19.34 0.07 19.34
## technologies 39 0.07 19.42 0.07 19.42
## cluster analysis 38 0.07 19.49 0.07 19.49
## data visualization 38 0.07 19.56 0.07 19.56
## day 38 0.07 19.63 0.07 19.63
## feature engineering 38 0.07 19.71 0.07 19.71
## nlp 38 0.07 19.78 0.07 19.78
## optimization 38 0.07 19.85 0.07 19.85
## unstructured data sets 38 0.07 19.92 0.07 19.92
## creativity 37 0.07 19.99 0.07 19.99
## equal opportunity 37 0.07 20.06 0.07 20.06
## health care 37 0.07 20.13 0.07 20.13
## machine learning algorithms 37 0.07 20.20 0.07 20.20
## numpy 37 0.07 20.27 0.07 20.27
## scikit 37 0.07 20.35 0.07 20.35
## time series analysis 37 0.07 20.42 0.07 20.42
## applications 36 0.07 20.48 0.07 20.48
## duties 36 0.07 20.55 0.07 20.55
## individuals 36 0.07 20.62 0.07 20.62
## operations research 36 0.07 20.69 0.07 20.69
## outliers 36 0.07 20.76 0.07 20.76
## predictive analytics 36 0.07 20.83 0.07 20.83
## interpreting data 35 0.07 20.89 0.07 20.89
## keras 35 0.07 20.96 0.07 20.96
## knowledge 35 0.07 21.03 0.07 21.03
## machine learning techniques 35 0.07 21.09 0.07 21.09
## master's degree 35 0.07 21.16 0.07 21.16
## mission 35 0.07 21.23 0.07 21.23
## support 35 0.07 21.29 0.07 21.29
## technology 35 0.07 21.36 0.07 21.36
## work 35 0.07 21.43 0.07 21.43
## concepts 34 0.06 21.49 0.06 21.49
## masters 34 0.06 21.56 0.06 21.56
## simulation methods 34 0.06 21.62 0.06 21.62
## success 34 0.06 21.69 0.06 21.69
## ideas 33 0.06 21.75 0.06 21.75
## public opinion 33 0.06 21.81 0.06 21.81
## public policy 33 0.06 21.87 0.06 21.87
## technical skills 33 0.06 21.94 0.06 21.94
## advanced mathematical techniques 32 0.06 22.00 0.06 22.00
## analysis of variance 32 0.06 22.06 0.06 22.06
## appropriate methods 32 0.06 22.12 0.06 22.12
## bagging methods 32 0.06 22.18 0.06 22.18
## bootstrap 32 0.06 22.24 0.06 22.24
## business case analysis 32 0.06 22.30 0.06 22.30
## challenging research needs 32 0.06 22.36 0.06 22.36
## correlation 32 0.06 22.42 0.06 22.42
## data capture firm 32 0.06 22.49 0.06 22.49
## graphical representation 32 0.06 22.55 0.06 22.55
## growing team of skilled data capture professionals 32 0.06 22.61 0.06 22.61
## information technology field of study 32 0.06 22.67 0.06 22.67
## insight generation 32 0.06 22.73 0.06 22.73
## large datasets 32 0.06 22.79 0.06 22.79
## leading data-driven survey research 32 0.06 22.85 0.06 22.85
## logistics regression 32 0.06 22.91 0.06 22.91
## modeling process 32 0.06 22.97 0.06 22.97
## monte carlo 32 0.06 23.03 0.06 23.03
## non-partisan research areas 32 0.06 23.09 0.06 23.09
## nosql dbs 32 0.06 23.16 0.06 23.16
## prescriptive analytic solutions 32 0.06 23.22 0.06 23.22
## problem identification 32 0.06 23.28 0.06 23.28
## pytorch 32 0.06 23.34 0.06 23.34
## quantitative modeling 32 0.06 23.40 0.06 23.40
## reports of data metrics 32 0.06 23.46 0.06 23.46
## research outcomes 32 0.06 23.52 0.06 23.52
## rigorous data capture 32 0.06 23.58 0.06 23.58
## srss 32 0.06 23.64 0.06 23.64
## ssrs data scientist 32 0.06 23.70 0.06 23.70
## successful modeling 32 0.06 23.76 0.06 23.76
## team members 32 0.06 23.83 0.06 23.83
## technical bachelor 32 0.06 23.89 0.06 23.89
## united states 32 0.06 23.95 0.06 23.95
## value 32 0.06 24.01 0.06 24.01
## year degree 32 0.06 24.07 0.06 24.07
## years of related work experience 32 0.06 24.13 0.06 24.13
## analyses 31 0.06 24.19 0.06 24.19
## commitment 31 0.06 24.25 0.06 24.25
## data mining techniques 31 0.06 24.31 0.06 24.31
## desire 31 0.06 24.37 0.06 24.37
## multiple sources 31 0.06 24.43 0.06 24.43
## non-technical audiences 31 0.06 24.48 0.06 24.48
## affirmative action employer 30 0.06 24.54 0.06 24.54
## finance 30 0.06 24.60 0.06 24.60
## marketing 30 0.06 24.66 0.06 24.66
## statistical methods 30 0.06 24.71 0.06 24.71
## statistical models 30 0.06 24.77 0.06 24.77
## training 30 0.06 24.83 0.06 24.83
## advantage 29 0.06 24.88 0.06 24.88
## business problems 29 0.06 24.94 0.06 24.94
## development 29 0.06 24.99 0.06 24.99
## findings 29 0.06 25.05 0.06 25.05
## member 29 0.06 25.10 0.06 25.10
## skills 29 0.06 25.16 0.06 25.16
## testing 29 0.06 25.21 0.06 25.21
## artificial intelligence 28 0.05 25.27 0.05 25.27
## country 28 0.05 25.32 0.05 25.32
## disabilities 28 0.05 25.37 0.05 25.37
## expression 28 0.05 25.43 0.05 25.43
## minimum 28 0.05 25.48 0.05 25.48
## position summary 28 0.05 25.53 0.05 25.53
## analysis 27 0.05 25.58 0.05 25.58
## best practices 27 0.05 25.64 0.05 25.64
## bs 27 0.05 25.69 0.05 25.69
## diversity 27 0.05 25.74 0.05 25.74
## methodologies 27 0.05 25.79 0.05 25.79
## needs 27 0.05 25.84 0.05 25.84
## travel 27 0.05 25.89 0.05 25.89
## verbal communication skills 27 0.05 25.94 0.05 25.94
## interpersonal skills 26 0.05 25.99 0.05 25.99
## knowledge of statistics 26 0.05 26.04 0.05 26.04
## programming languages 26 0.05 26.09 0.05 26.09
## stakeholders 26 0.05 26.14 0.05 26.14
## validation 26 0.05 26.19 0.05 26.19
## vision 26 0.05 26.24 0.05 26.24
## written communication skills 26 0.05 26.29 0.05 26.29
## addition 25 0.05 26.34 0.05 26.34
## advanced degree 25 0.05 26.39 0.05 26.39
## businesses 25 0.05 26.43 0.05 26.43
## curiosity 25 0.05 26.48 0.05 26.48
## customer 25 0.05 26.53 0.05 26.53
## data science team 25 0.05 26.58 0.05 26.58
## disability status 25 0.05 26.62 0.05 26.62
## genetic information 25 0.05 26.67 0.05 26.67
## implementation 25 0.05 26.72 0.05 26.72
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## pipelines 11 0.02 41.22 0.02 41.22
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## team player 11 0.02 41.60 0.02 41.60
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## knowledge of sql 10 0.02 42.43 0.02 42.43
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## data analysis professional 8 0.02 45.52 0.02 45.52
## data architectures 8 0.02 45.54 0.02 45.54
## data cleaning 8 0.02 45.55 0.02 45.55
## data lake 8 0.02 45.57 0.02 45.57
## data pipelines 8 0.02 45.58 0.02 45.58
## data science bowl 8 0.02 45.60 0.02 45.60
## data science leaders 8 0.02 45.61 0.02 45.61
## data visualization techniques 8 0.02 45.63 0.02 45.63
## decision forests 8 0.02 45.64 0.02 45.64
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## dli 8 0.02 45.74 0.02 45.74
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## drawbacks 8 0.02 45.78 0.02 45.78
## effectiveness of different courses of action 8 0.02 45.80 0.02 45.80
## employees' differences 8 0.02 45.81 0.02 45.81
## end-to-end solutions 8 0.02 45.83 0.02 45.83
## entry level 8 0.02 45.84 0.02 45.84
## equal opportunity-affirmative action employer· 8 0.02 45.86 0.02 45.86
## equivalent combination of education 8 0.02 45.87 0.02 45.87
## existing data 8 0.02 45.89 0.02 45.89
## existing data sources 8 0.02 45.90 0.02 45.90
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## fields 8 0.02 45.93 0.02 45.93
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## good applied statistics skills 8 0.02 46.03 0.02 46.03
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## impactful reporting 8 0.02 46.12 0.02 46.12
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## information puzzle 8 0.02 46.16 0.02 46.16
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## llc 8 0.02 46.31 0.02 46.31
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## manner 8 0.02 46.35 0.02 46.35
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## model inputs 8 0.02 46.44 0.02 46.44
## monitor outcomes 8 0.02 46.45 0.02 46.45
## mxnet 8 0.02 46.47 0.02 46.47
## national intelligence· 8 0.02 46.48 0.02 46.48
## neo4j 8 0.02 46.50 0.02 46.50
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## nvidia 8 0.02 46.53 0.02 46.53
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## onsite training 8 0.02 46.56 0.02 46.56
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## party sources of information 8 0.02 46.60 0.02 46.60
## physical demands 8 0.02 46.62 0.02 46.62
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## power of analytics 8 0.02 46.65 0.02 46.65
## powering america 8 0.02 46.67 0.02 46.67
## practices 8 0.02 46.68 0.02 46.68
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## public sectors· 8 0.02 46.88 0.02 46.88
## quick learner 8 0.02 46.89 0.02 46.89
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## response 8 0.02 46.92 0.02 46.92
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## right combination of tools 8 0.02 46.99 0.02 46.99
## risk modeling 8 0.02 47.00 0.02 47.00
## s premier data science 8 0.02 47.02 0.02 47.02
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## sap 8 0.02 47.06 0.02 47.06
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## functional area 7 0.01 48.19 0.01 48.19
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## google analytics 7 0.01 48.22 0.01 48.22
## google cloud 7 0.01 48.23 0.01 48.23
## high quality prediction systems 7 0.01 48.25 0.01 48.25
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## in-depth analysis 7 0.01 48.27 0.01 48.27
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## managers 7 0.01 48.43 0.01 48.43
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## validated stories 7 0.01 49.30 0.01 49.30
## vast amounts of data 7 0.01 49.31 0.01 49.31
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## butterfly network 6 0.01 49.93 0.01 49.93
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## client needs 4 0.01 57.52 0.01 57.52
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## desk 4 0.01 57.99 0.01 57.99
## detailed set of results 4 0.01 58.00 0.01 58.00
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## device 4 0.01 58.01 0.01 58.01
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## drinks 4 0.01 58.15 0.01 58.15
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## eda 4 0.01 58.17 0.01 58.17
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## efficiencies 4 0.01 58.21 0.01 58.21
## electronic health records 4 0.01 58.22 0.01 58.22
## emerging customer needs 4 0.01 58.23 0.01 58.23
## employer of choice 4 0.01 58.23 0.01 58.23
## employment individuals 4 0.01 58.24 0.01 58.24
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## end 4 0.01 58.26 0.01 58.26
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## enormous volumes of data 4 0.01 58.29 0.01 58.29
## environment 4 0.01 58.29 0.01 58.29
## environments 4 0.01 58.30 0.01 58.30
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## essential duties 4 0.01 58.32 0.01 58.32
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## flume 4 0.01 58.44 0.01 58.44
## forefront 4 0.01 58.45 0.01 58.45
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## game 4 0.01 58.51 0.01 58.51
## ged 4 0.01 58.52 0.01 58.52
## general nature 4 0.01 58.52 0.01 58.52
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## generous compensation package 4 0.01 58.54 0.01 58.54
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## geospatial data 4 0.01 58.55 0.01 58.55
## giant objective 4 0.01 58.56 0.01 58.56
## global reach 4 0.01 58.57 0.01 58.57
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## gradient boosting 4 0.01 58.58 0.01 58.58
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## greenplum 4 0.01 58.61 0.01 58.61
## groups 4 0.01 58.61 0.01 58.61
## growth opportunities 4 0.01 58.62 0.01 58.62
## guide health 4 0.01 58.63 0.01 58.63
## hadoop platform 4 0.01 58.64 0.01 58.64
## hard work 4 0.01 58.64 0.01 58.64
## harvesting insights 4 0.01 58.65 0.01 58.65
## healthier atmosphere 4 0.01 58.66 0.01 58.66
## high-dimensionality data 4 0.01 58.67 0.01 58.67
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## high quality 4 0.01 58.69 0.01 58.69
## high school diploma 4 0.01 58.70 0.01 58.70
## hiring 4 0.01 58.71 0.01 58.71
## historical data 4 0.01 58.71 0.01 58.71
## homeland security 4 0.01 58.72 0.01 58.72
## hour work week 4 0.01 58.73 0.01 58.73
## houston 4 0.01 58.74 0.01 58.74
## hundreds of thousands 4 0.01 58.74 0.01 58.74
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## illinois 4 0.01 58.76 0.01 58.76
## imagination 4 0.01 58.77 0.01 58.77
## immediate impact 4 0.01 58.77 0.01 58.77
## improved service desk performance 4 0.01 58.78 0.01 58.78
## inclusive culture 4 0.01 58.79 0.01 58.79
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## inclusiveness 4 0.01 58.80 0.01 58.80
## incredible company 4 0.01 58.81 0.01 58.81
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## jema 4 0.01 58.93 0.01 58.93
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## key trends 4 0.01 58.96 0.01 58.96
## knowledge managers 4 0.01 58.97 0.01 58.97
## knowledge of commercial cloud services 4 0.01 58.98 0.01 58.98
## knowledge of data analysis 4 0.01 58.99 0.01 58.99
## knowledge of descriptive 4 0.01 59.00 0.01 59.00
## knowledge of programming java 4 0.01 59.00 0.01 59.00
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## largest independent technology 4 0.01 59.02 0.01 59.02
## latest technology trends 4 0.01 59.03 0.01 59.03
## launch 4 0.01 59.03 0.01 59.03
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## leading role 4 0.01 59.05 0.01 59.05
## level 4 0.01 59.06 0.01 59.06
## level of experience 4 0.01 59.06 0.01 59.06
## level of work 4 0.01 59.07 0.01 59.07
## life's best work 4 0.01 59.08 0.01 59.08
## life balance 4 0.01 59.09 0.01 59.09
## lifestyle choices 4 0.01 59.09 0.01 59.09
## lightweight web applications 4 0.01 59.10 0.01 59.10
## limitations of models 4 0.01 59.11 0.01 59.11
## limitless opportunities 4 0.01 59.12 0.01 59.12
## linux shell script 4 0.01 59.12 0.01 59.12
## ll need 4 0.01 59.13 0.01 59.13
## loading 4 0.01 59.14 0.01 59.14
## local law 4 0.01 59.15 0.01 59.15
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## long term disability 4 0.01 59.17 0.01 59.17
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## lstm 4 0.01 59.19 0.01 59.19
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## manager 4 0.01 59.22 0.01 59.22
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## markets 4 0.01 59.24 0.01 59.24
## maryland 4 0.01 59.25 0.01 59.25
## massage 4 0.01 59.25 0.01 59.25
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## maxar technologies 4 0.01 59.28 0.01 59.28
## measurement 4 0.01 59.28 0.01 59.28
## medicare 4 0.01 59.29 0.01 59.29
## member engagement 4 0.01 59.30 0.01 59.30
## mentoring 4 0.01 59.31 0.01 59.31
## merchandising 4 0.01 59.31 0.01 59.31
## millions of consumers 4 0.01 59.32 0.01 59.32
## mind 4 0.01 59.33 0.01 59.33
## minimum education 4 0.01 59.34 0.01 59.34
## mission partners 4 0.01 59.35 0.01 59.35
## model building 4 0.01 59.35 0.01 59.35
## model feasibility research 4 0.01 59.36 0.01 59.36
## model validation 4 0.01 59.37 0.01 59.37
## modern software languages 4 0.01 59.38 0.01 59.38
## money 4 0.01 59.38 0.01 59.38
## monitoring 4 0.01 59.39 0.01 59.39
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## new models 4 0.01 59.47 0.01 59.47
## new techniques 4 0.01 59.48 0.01 59.48
## new uses 4 0.01 59.49 0.01 59.49
## night 4 0.01 59.50 0.01 59.50
## nosql data structures 4 0.01 59.51 0.01 59.51
## novel algorithms 4 0.01 59.51 0.01 59.51
## openness 4 0.01 59.52 0.01 59.52
## operational issues 4 0.01 59.53 0.01 59.53
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## optum 4 0.01 59.54 0.01 59.54
## oracle sql 4 0.01 59.55 0.01 59.55
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## oversight 4 0.01 59.57 0.01 59.57
## ownership 4 0.01 59.57 0.01 59.57
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## perspectives 4 0.01 59.62 0.01 59.62
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## physicians 4 0.01 59.63 0.01 59.63
## platforms hadoop eco system 4 0.01 59.64 0.01 59.64
## player 4 0.01 59.65 0.01 59.65
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## range of existing big data technologies 4 0.01 59.84 0.01 59.84
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## reach 4 0.01 59.86 0.01 59.86
## real-world data problems 4 0.01 59.86 0.01 59.86
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## recruiting 4 0.01 59.88 0.01 59.88
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## rnns 4 0.01 59.98 0.01 59.98
## roadblocks 4 0.01 59.99 0.01 59.99
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## secure website 4 0.01 60.08 0.01 60.08
## selected candidate 4 0.01 60.09 0.01 60.09
## semantic discrepancies 4 0.01 60.10 0.01 60.10
## senior executive audience 4 0.01 60.11 0.01 60.11
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## shell 4 0.01 60.12 0.01 60.12
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## similar field 4 0.01 60.14 0.01 60.14
## similar role 4 0.01 60.15 0.01 60.15
## single day 4 0.01 60.15 0.01 60.15
## singular opportunity 4 0.01 60.16 0.01 60.16
## site catalyst 4 0.01 60.17 0.01 60.17
## sizes 4 0.01 60.18 0.01 60.18
## social listening 4 0.01 60.18 0.01 60.18
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## software skills 4 0.01 60.21 0.01 60.21
## solid understanding of data storage structures 4 0.01 60.21 0.01 60.21
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## staff 4 0.01 60.24 0.01 60.24
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## strong mathematical background 4 0.01 60.38 0.01 60.38
## strong passion 4 0.01 60.39 0.01 60.39
## strong team player 4 0.01 60.40 0.01 60.40
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## style 4 0.01 60.43 0.01 60.43
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## title 4 0.01 60.57 0.01 60.57
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## torch 4 0.01 60.59 0.01 60.59
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## trust 4 0.01 60.65 0.01 60.65
## turn 4 0.01 60.66 0.01 60.66
## undiscovered solutions 4 0.01 60.66 0.01 60.66
## unitedhealth group family of businesses 4 0.01 60.67 0.01 60.67
## unstructured datasets 4 0.01 60.68 0.01 60.68
## unsupervised machine learning algorithms 4 0.01 60.69 0.01 60.69
## usa 4 0.01 60.69 0.01 60.69
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## vet 4 0.01 60.76 0.01 60.76
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## visibility 4 0.01 60.78 0.01 60.78
## vision insurance 4 0.01 60.78 0.01 60.78
## visitors 4 0.01 60.79 0.01 60.79
## voice 4 0.01 60.80 0.01 60.80
## warehouse 4 0.01 60.81 0.01 60.81
## web analytics 4 0.01 60.82 0.01 60.82
## welcome 4 0.01 60.82 0.01 60.82
## windows 4 0.01 60.83 0.01 60.83
## word 4 0.01 60.84 0.01 60.84
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## world's large accumulation of health 4 0.01 60.88 0.01 60.88
## written formats 4 0.01 60.88 0.01 60.88
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## years technical 1 0.00 99.98 0.00 99.98
## yield 1 0.00 99.98 0.00 99.98
## young learners stem skills 1 0.00 99.98 0.00 99.98
## youto 1 0.00 99.99 0.00 99.99
## ypsilanti 1 0.00 99.99 0.00 99.99
## yrs experience 1 0.00 99.99 0.00 99.99
## zendesk 1 0.00 99.99 0.00 99.99
## zeno 1 0.00 99.99 0.00 99.99
## zip code 1 0.00 100.00 0.00 100.00
## zoominfo 1 0.00 100.00 0.00 100.00
## zoomph 1 0.00 100.00 0.00 100.00
## <NA> 0 0.00 100.00
## Total 52523 100.00 100.00 100.00 100.00
Tidy the job descriptions
Before we continue, we have to tidy the job descriptions. We will remove non-ASCII characters, remove some unecessary characters, stop words, make the text lowercase, and white space. Stop words are common words such as the, is, at, which, etc. This will be handy in our analysis.
#Remove non-ASCII characters
descriptions <- gsub("[^\x20-\x7E]", " ", descriptions)
#Convert our variable to a Corpus variable for the package tm to work on.
#A corpus is a collection of documents
descriptions <- Corpus(VectorSource(descriptions))
#Create a function to make patterns into spaces
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
descriptions <- tm_map(descriptions, toSpace, "/")
descriptions <- tm_map(descriptions, toSpace, "\n")
descriptions <- tm_map(descriptions, toSpace, "@")
descriptions <- tm_map(descriptions, toSpace, "\\|")
#Convert the text to lowercase
descriptions <- tm_map(descriptions, content_transformer(tolower))
#Remove English stop words
descriptions <- tm_map(descriptions, removeWords, stopwords("english"))
#Remove punctuation , ., etc.
descriptions <- tm_map(descriptions, removePunctuation)
#Remove whitespace
descriptions <- tm_map(descriptions, stripWhitespace)Text Mining
After we have imported and tidied our data, we can begin to perform tests to mine it.
Words by Frequency
#Create a term document matrix based upon the variable descriptions
tdm <- TermDocumentMatrix(descriptions)
m <- as.matrix(tdm)
v <- sort(rowSums(m),decreasing=TRUE)
#Creates a frequency table for words
freqTable <- data.frame(word = names(v), freq=v)
Here is a frequency table and barplot for words found in job descriptions:
freqTable#Bar plot of the 20 most common words
barplot(v[1:20], col = "#003366", las = 2)
Word Cloud
A Word Cloud Based Upon the Job Descriptions3
set.seed(1234)
wordcloud(words = freqTable$word, freq = freqTable$freq, min.freq = 100,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
Analysis
Conclusions
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-call-api↩
Note the image was created with stop words on https://github.com/logicalschema/DATA607/raw/master/project3/images/stopwords.jpg↩
Reference code was found here: http://www.sthda.com/english/wiki/text-mining-and-word-cloud-fundamentals-in-r-5-simple-steps-you-should-know↩
